Data Science at Made.com
MADE.com is a UK-based online retailer with an award-winning business model. Foregoing traditional storefronts, Made has only three UK-based showrooms and works directly with designers and manufacturers to produce high-quality, thoughtfully designed furniture at remarkable prices.
As a made-to-order online company with furniture moving from ship to lounge room, leveraging production and customer data is critical to managing their supply chain. If too many products are made the company loses money is dock or storage fees; too little and customers wait longer to receive the orders. Made also prides itself on keeping ahead of trends, so predicting customer behaviour is vital to their business.
What I love most about Made.com is their TalentLAB crowdfunding platform. It’s a place to discover new talent and unique products you won’t find anywhere else. TalentLAB was born out of the MADE Emerging Talent Award – an annual competition for up-and-coming designers to break into the industry, and get their product made and sold.
An evening of networking & talks
We had the privilege of spending the evening with Made’s CEO, Talent Acquisition team, and Data Science team. Being a design focussed retailer, it should come as no surprise that visiting Made’s London Headquarters was a really lovely experience. We were welcomed with drinks and nibbles, and more food following talks from Made’s leaders. Talks focussed on Made’s current and future data science aspirations, and why growing their team is critical to their future success.
Farhan Miah – Head of Acquisition
Bulat Yapparov – Head of Analytics, and
Sarah Fairbairn – Culture Club Representative
Where does Data Science fit in?
Understanding the Customer Lifecycle
Considered purchases means long sales cycles.
Cross device browsing is hard to track – but here to stay
User journeys do not start at their site, they start in search engines.
There is untapped potential to drive repeat purchases.
Accurate sales forecasting is key to good lead times
Predicting forecast hits at the design stage could be game changing
The customer service team currently scales with sales
Advances in AI means many simple customer service queries should be able to be automated.
Visual search – finding products of a similar colour, shape, or style.
Identifying products that work well together.
Learning to understand a customers style.